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Neural Embedding Compression For Efficient Multi-Task Earth Observation
  Modelling

Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling

26 March 2024
Carlos Gomes
Thomas Brunschwiler
ArXivPDFHTML

Papers citing "Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling"

4 / 4 papers shown
Title
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial
  Representation Learning
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
Colorado Reed
Ritwik Gupta
Shufan Li
S. Brockman
Christopher Funk
Brian Clipp
Kurt Keutzer
Salvatore Candido
M. Uyttendaele
Trevor Darrell
121
169
0
30 Dec 2022
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
308
7,443
0
11 Nov 2021
End-to-end Learning of Compressible Features
End-to-end Learning of Compressible Features
Saurabh Singh
Sami Abu-El-Haija
Nick Johnston
Johannes Ballé
Abhinav Shrivastava
G. Toderici
SSL
97
71
0
23 Jul 2020
Real-Time Single Image and Video Super-Resolution Using an Efficient
  Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi
Jose Caballero
Ferenc Huszár
J. Totz
Andrew P. Aitken
Rob Bishop
Daniel Rueckert
Zehan Wang
SupR
198
5,176
0
16 Sep 2016
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